import torch

import torch.nn as nn

import torchvision.transforms.functional as TF

class DoubleConvolution(nn.Module):

    def __init__(self, in_channels, out_channels):

        super(DoubleConvolution, self).__init__()

        self.conv = nn.Sequential(

            nn.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False),

            # Although in the original paper, these convolutions are unpadded but we didn't do it here.

            nn.BatchNorm2d(out_channels),

            nn.ReLU(inplace=True),

            nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=False),

            nn.BatchNorm2d(out_channels),

            nn.ReLU(inplace=True)

        )

    def forward(self, x):

        return self.conv(x)

class UNET(nn.Module):

    def __init__(self, in_channels=3, out_channels=1, features = [64, 128, 256, 512]):

        super(UNET, self).__init__()

        self.downs = nn.ModuleList()

        self.ups = nn.ModuleList()

        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

        for feature in features:

            self.downs.append(DoubleConvolution(in_channels, feature))

            in_channels = feature

        for feature in reversed(features):

            self.ups.append(

                nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride=2)

            )

            self.ups.append(DoubleConvolution(feature*2, feature))

        self.bottleneck = DoubleConvolution(features[-1], features[-1]*2)

        self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)

    def forward(self, x):

        skip_connections = []

        for down in self.downs:

            x = down(x)

            skip_connections.append(x)

            x = self.pool(x)

        x = self.bottleneck(x)

        skip_connections = skip_connections[::-1]

        for idx in range(0, len(self.ups), 2):

            x = self.ups[idx](x)

            skip_connection = skip_connections[idx//2]

            if x.shape != skip_connection.shape:

                x = TF.resize(x, size=skip_connection.shape[2:])

            concat_skip = torch.cat((skip_connection, x), dim=1)

            x = self.ups[idx+1](concat_skip)

        return self.final_conv(x)